论文标题
对镰状细胞疾病患者的自动脾长度测量的深度学习
Deep Learning for Automatic Spleen Length Measurement in Sickle Cell Disease Patients
论文作者
论文摘要
镰状细胞疾病（SCD）是世界上最常见的遗传疾病之一。 SCD儿童经常出现脾肿大（脾脏的异常增大）。如果未经治疗，脾肿大可能会威胁生命。当前测量脾脏大小的工作流程包括触诊，然后可能在2D超声成像中进行手动长度测量。但是，此手动测量取决于操作员的专业知识，并受到观察者间和观察者间的可变性。我们研究了深度学习的使用来自动对超声图像的脾长度进行自动估计。我们研究了两种类型的方法，一种基于细分的方法，一种基于直接长度估计，并将结果与人类专家进行的测量进行比较。我们的最佳模型（基于细分）达到了7.42％的百分比误差，即接近观察者间变异性水平（5.47％-6.34％）。据我们所知，这是从超声图像中以完全自动化的方式测量脾脏大小的第一次尝试。
Sickle Cell Disease (SCD) is one of the most common genetic diseases in the world. Splenomegaly (abnormal enlargement of the spleen) is frequent among children with SCD. If left untreated, splenomegaly can be life-threatening. The current workflow to measure spleen size includes palpation, possibly followed by manual length measurement in 2D ultrasound imaging. However, this manual measurement is dependent on operator expertise and is subject to intra- and inter-observer variability. We investigate the use of deep learning to perform automatic estimation of spleen length from ultrasound images. We investigate two types of approach, one segmentation-based and one based on direct length estimation, and compare the results against measurements made by human experts. Our best model (segmentation-based) achieved a percentage length error of 7.42%, which is approaching the level of inter-observer variability (5.47%-6.34%). To the best of our knowledge, this is the first attempt to measure spleen size in a fully automated way from ultrasound images.
